





from ...processing_utils import ProcessorMixin


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''SpeechT5FeatureExtractor'''
     __UpperCamelCase            =  '''SpeechT5Tokenizer'''



     def __init__(				self							:Tuple      ,   snake_case							:Optional[Any]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        super().__init__(lowercase_      ,   lowercase_	)



     def __call__(				self							:Optional[Any]      ,   *snake_case							:List[str]      ,   **snake_case							:Dict	):





        '''simple docstring'''




        A_       :					List[str]        =      kwargs.pop("audio"      ,   lowercase_	)
        A_       :					Any        =      kwargs.pop("text"      ,   lowercase_	)
        A_       :					int        =      kwargs.pop("text_target"      ,   lowercase_	)
        A_       :					int        =      kwargs.pop("audio_target"      ,   lowercase_	)
        A_       :					Optional[Any]        =      kwargs.pop("sampling_rate"      ,   lowercase_	)

        if audio is not None and text is not None:
           raise ValueError(
               "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"	)
        if audio_target is not None and text_target is not None:
           raise ValueError(
               "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"	)
        if audio is None and audio_target is None and text is None and text_target is None:
           raise ValueError(
               "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."	)

        if audio is not None:
           A_       :					Dict        =      self.feature_extractor(lowercase_      ,   *lowercase_      ,   sampling_rate=lowercase_      ,   **lowercase_	)
        elif text is not None:
           A_       :					Optional[int]        =      self.tokenizer(lowercase_      ,   **lowercase_	)
        else:
           A_       :					str        =      None

        if audio_target is not None:
           A_       :					List[str]        =      self.feature_extractor(audio_target=lowercase_      ,   *lowercase_      ,   sampling_rate=lowercase_      ,   **lowercase_	)
           A_       :					List[str]        =      targets["input_values"]
        elif text_target is not None:
           A_       :					Optional[Any]        =      self.tokenizer(lowercase_      ,   **lowercase_	)
           A_       :					str        =      targets["input_ids"]
        else:
           A_       :					Optional[int]        =      None

        if inputs is None:
           return targets

        if targets is not None:
           A_       :					Optional[int]        =      labels

           A_       :					Dict        =      targets.get("attention_mask"	)
           if decoder_attention_mask is not None:
              A_       :					Dict        =      decoder_attention_mask

        return inputs



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   *snake_case							:int      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					str        =      kwargs.pop("input_values"      ,   lowercase_	)
        A_       :					Tuple        =      kwargs.pop("input_ids"      ,   lowercase_	)
        A_       :					List[Any]        =      kwargs.pop("labels"      ,   lowercase_	)

        if input_values is not None and input_ids is not None:
           raise ValueError("Cannot process both `input_values` and `input_ids` inputs."	)
        if input_values is None and input_ids is None and labels is None:
           raise ValueError(
               "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."	)

        if input_values is not None:
           A_       :					Optional[int]        =      self.feature_extractor.pad(lowercase_      ,   *lowercase_      ,   **lowercase_	)
        elif input_ids is not None:
           A_       :					Union[str, Any]        =      self.tokenizer.pad(lowercase_      ,   **lowercase_	)
        else:
           A_       :					Union[str, Any]        =      None

        if labels is not None:
           if "input_ids" in labels or (isinstance(lowercase_      ,   lowercase_	) and "input_ids" in labels[0]):
              A_       :					Optional[int]        =      self.tokenizer.pad(lowercase_      ,   **lowercase_	)
              A_       :					Dict        =      targets["input_ids"]
           else:
              A_       :					List[Any]        =      self.feature_extractor.feature_size
              A_       :					List[Any]        =      self.feature_extractor.num_mel_bins
              A_       :					Optional[Any]        =      self.feature_extractor.pad(lowercase_      ,   *lowercase_      ,   **lowercase_	)
              A_       :					Optional[Any]        =      feature_size_hack
              A_       :					str        =      targets["input_values"]
        else:
           A_       :					Any        =      None

        if inputs is None:
           return targets

        if targets is not None:
           A_       :					int        =      labels

           A_       :					Tuple        =      targets.get("attention_mask"	)
           if decoder_attention_mask is not None:
              A_       :					List[Any]        =      decoder_attention_mask

        return inputs



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   *snake_case							:Any      ,   **snake_case							:Dict	):





        '''simple docstring'''




        return self.tokenizer.batch_decode(*lowercase_      ,   **lowercase_	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   *snake_case							:Tuple      ,   **snake_case							:Union[str, Any]	):





        '''simple docstring'''




        return self.tokenizer.decode(*lowercase_      ,   **lowercase_	)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




